Context and approach

The report argues that while individual technologies like AI, cloud computing, and DLT are powerful, their strategic clustering will transform the financial services industry by altering competitive dynamics and operating models. It identifies four primary innovation pathways: establishing ecosystems beyond finance, integrating physical and digital processes, reorienting transaction flows, and reimagining core functions. The document details specific use cases across lending, investment management, payments, insurance, and market infrastructure, while warning of systemic risks including increased fraud, liquidity challenges, and ethical concerns regarding AI explainability and data privacy.

• The competitive standard in financial services is rising as transformative technologies become widely accessible, shifting sustainable differentiation from operational efficiency to core capabilities in assembly, execution, and relationship management.
• Control over emerging technologies is shifting toward non-financial service providers, particularly large cloud providers who dominate AI-as-a-service and will likely lead in quantum-as-a-service, creating new interdependencies for financial firms.
• Financial institutions can capture value quickly by implementing use cases that combat data fragmentation through knowledge graphs and accelerate feedback loops to anticipate customer needs in real time.
• Traditional industry barriers are deteriorating as AI enables autonomous code writing and 'gigification' of talent, while Privacy Enhancing Techniques (PETs) and DLT improve data portability and auditability.
• The transition to real-time interactions and disintermediation introduces systemic risks, such as increased liquidity costs for instant securities settlement and a rise in fraud due to the speed of real-time payments.
• Malicious actors are utilizing the same emerging technologies as financial institutions to create new attack vectors, including AI-generated deepfakes for spoofing and the exploitation of IoT device vulnerabilities.
• Emerging technologies introduce ethical and social challenges, including the inherent unexplainability of quantum machine learning algorithms and the potential for cloud-based market concentration.
• The report identifies four key innovation pathways: establishing ecosystems beyond finance, integrating physical and digital processes, reorienting transaction flows, and reimagining core functions.
• In the lending sector, 'just-in-time lending' uses IoT and AI to provide proactive funding and advice to SMEs based on real-time operational data.
• In investment management, 'verifiable impact investing' leverages DLT and IoT to allow SMEs to issue investment tokens linked to verified sustainability data.
• The payments sector is moving toward machine-to-machine (M2M) protocols where AI agents negotiate and execute transactions autonomously via smart contracts.
• In insurance, 'dynamic life + health insurance' uses real-time data from wearables and IoT to personalize premiums and rewards based on healthy behaviors.
• Market infrastructure can be improved through a 'distributed KYC/AML utility' that uses DLT and PETs to reduce redundant onboarding efforts across multiple institutions.

APA

World Economic Forum (n.d.). Context and approach. http://www3.weforum.org/docs/WEF_Forging_New_Pathways_2020.pdf

Chicago

World Economic Forum. Context and approach. n.d. http://www3.weforum.org/docs/WEF_Forging_New_Pathways_2020.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Context and approach |url=http://www3.weforum.org/docs/WEF_Forging_New_Pathways_2020.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforumndcontext, author = {{World Economic Forum}}, title = {{Context and approach}}, institution = {World Economic Forum}, url = {http://www3.weforum.org/docs/WEF_Forging_New_Pathways_2020.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }